Elsevier

Tribology International

Volume 127, November 2018, Pages 446-456
Tribology International

Influence of preload control on friction force measurement of fabric samples

https://doi.org/10.1016/j.triboint.2018.06.007Get rights and content

Highlights

  • Psychophysical tests were conducted on different surfaces in order to show the regulating behavior of human beings.

  • Friction induced vibration on textile fabrics was investigated using an artificial finger with force feedback capability.

  • Constant preload experiments reveal the fundamental frequency of the pattern and capable of filtering the other frequencies.

  • Maintaining the normal load constant compensated for the large height change during the sliding experiments.

  • The non-linear behavior between normal load and friction was primarily determined by the friction index.

Abstract

Tactile psychophysical tests were conducted on different surfaces to observe the regulating behavior of human beings. Subsequently, friction induced vibration on textile fabrics was investigated using an artificial finger experimental setup capable of maintaining a constant preload on the substrate. To obtain the constant preload, three different types of controllers were implemented and compared. Kinematic experiments were conducted for repetitive fabric textures with different normal loads and velocities to find the friction force maps. The constant preloading experiments revealed the dominant frequency of the textile pattern and were capable of filtering the other frequencies. Moreover, the artificial finger was able to detect a spatial texture pattern with an error of less than 4.4%, as compared to the values derived from image processing.

Introduction

Explicit object properties, such as the shape and physical dimensions, may be recognized by human beings only through visual sensation. However, the detailed properties of objects, such as the compliance, texture roughness, and temperature can only be identified by touch. Indeed, the dexterous and stable grasp or exploration of any object by human beings requires a collaborative effort of the touch and vision senses, and may even require additional sensing, such as auditory sensations [[1], [2], [3], [4], [5]]. Therefore, recently, sophisticated sensory systems, such as tactile, vision, inertial, and auditory feedback systems, are being included in the development of humanoid robots [[6], [7], [8], [9]].

Different parts of the human skin are proofed to have diverse sensitivity. The most sensitive parts of the human body are the fingers, whose outer layer is called epidermis, which serves as a protecting layer for the inner dermis layer, and comprises the papillary ridge. Recently, the papillary ridge, which contains the curly fingerprint pattern period, was shown to have a possible effect on the perceptual ability of textures [10]. It has been shown that the spatial period of the texture induces vibrations corresponding to the fingerprint spatial period, and this fact may amplify the transmitted stress signal to the inner layer. The receptors are located inside the dermis, which is a viscoelastic medium owing to the extracellular matrix (ECM). Different receptors, such as the mechanoreceptors, thermoceptors, and nociceptors, are available in the human fingers, and are usually classified according to their task specialization during the cutaneous process [11,12]. Among them, the mechanoreceptors are responsible for mechanical perception, whereby, a complex mechanotransduction mechanism employs the multiple layers of the human finger [13].

When a human finger touches or moves on any surface, the stimulus due to the contact causes a local deformation at the interface, which leads to the distortion of a group of receptors located in the dermis layer [[14], [15], [16], [17], [18], [19], [20]]. The receptors with different characteristics and location of the touching area acquire dynamic stress, which amplifies and transmits the frequency and amplitude information and other data, such as temperature, to the brain for further processing. Various different receptor types exhibit different characteristics as a response to stimuli, typically in the range of 0.4–1000 Hz. The four main mechanoreceptors are the Merkel cells, and the Meissner, Ruffini, and Pacinian corpuscles [21]. Briefly, Merkel and Meissner receptors are located near the epidermis and are responsible for the detection of local stress, whereas, the Ruffini and Pacinian receptors are located in the deeper parts of the dermis and account for comprehensive stress envelopes. A further distinction of mechanoreceptors can be made with regard to their response speed to external stimuli [22,23]. The Merkel Cells and Ruffini corpuscles, which are widely known as the slow adapting (SA) cells, have slow response characteristics with an approximate frequency bandwidth of 0.4–100 Hz. The Meissner receptors are faster than the slow adapting cells and have an estimated bandwidth of 10–200 Hz, while the fastest response is attained by the Pacinian corpuscles, with an approximate frequency bandwidth of 0.4–100 Hz. It has been shown that the slow adapting mechanoreceptors are responsible for quasistatic touch, such as texture perception and grasping. On the contrary, the fast adapting mechanoreceptors are responsible for dynamic touch operations such as grip control and using an apparatus [13,19,24]. Indeed, the touching process is a self-regulating one, and is performed until the human is fully satisfied with decision making by touching the surface [25,26]. As an example, the human finger tracks the surface texture by changing the position in the vertical direction to apply a constant preload value to the examined surface.

Research activity related to the psychophysical haptic field and skin friction has focused on measuring normal and friction forces, and has resulted in obtaining the coefficient of friction (COF) of the skin and substrate under various sliding speeds [25,[27], [28], [29], [30], [31]]. In these studies, the experimental setups were usually arranged for use in linear or rotational relative movements to mimic the touch-stroke and multiple finger pinch handle exploratory movements of the human finger. Recently, a significant amount of experimental work was performed to understand the friction induced vibrations related to skin and substrate contact [[32], [33], [34], [35], [36], [37], [38]]. Some of the outcomes of these studies emphasized the importance of the surface texture, whereas, other outcomes indicated the prominence of the fingerprint effect for the induced vibrations on the contact interface. In fact, it has been found that the frequency response due to contact stimuli depends on both the texture of the surface and fingerprint geometry, in a complex manner, which can be combined with COF measurements to characterize the contact [34,39,40].

The very first step in ascertaining the quality of a fabric is hand discernment action. The handling of fabric significantly determines the saleability of textile products, and most consumers have implemented this practice before trying on garments. Moreover, the quality of textiles is still classified subjectively by highly trained specialists, who slide the index or thumb fingers over the fabrics produced by textile industries where textile quality characterization has not been automated [[41], [42], [43]]. A substantial amount of research has been conducted on mimicking the behavior of the human finger by an artificial finger. This method can be potentially used in many industrial fields such as the textile industry and robotics field, and requires similarly exceptional tactile skills as those of the human finger. Thus, widespread investigation on the design and implementation of artificial fingers is ongoing.

In relevant literature, artificial finger experimental setups can be categorized into three main groups according to the type of the probe used to contact the substrate: a) experimental setup with a rigid probe [41,42,[44], [45], [46], [47], [48]], b) experimental setup with a soft and replicated finger probe [[49], [50], [51], [52]], and c) experimental setup with a sensorized finger [[53], [54], [55], [56], [57], [58]]. In the first experimental-setup category, a rigid probe with different profiles contacts the substrates and applies a constant normal load during the friction experiments, preferably with different sliding velocities. In the second category of artificial finger experiments, a rigid probe is replaced by a soft one aiming at a similar mechanical behavior as the human finger. The last classification includes tribological experiments with probes having a single sensor or array. In reality, all the experimental setups or devices mentioned above, which aim at mimicking the human finger and substrate contact, utilize a dead weight or on-off control to apply a constant preload to the interface. Although the dead weight application or on-off control of the normal load is a straightforward method, it does not exactly resemble the condition of a human finger sliding on fabric. During the movement of the finger on the substrate, the human actually regulates the position of their finger on the surface by utilizing feedback while attempting to maintain a constant normal force until the assessment of substrate characteristics is completed.

In this study, an experimental setup was constructed to provide a constant preload during a sliding friction experiment. First, psychophysical tests were conducted with different surfaces to understand the regulating behavior of human beings. Then, three different types of controllers were designed and compared in terms of their ability to maintain a constant normal load on adhesive and fabric surfaces. When maintaining a constant preload by using a feedback control, signal harmonics were eliminated and frequencies higher than the fundamental frequency of the texture were filtered. The fundamental frequency of the fabric has a unique relationship to the surface scanning velocity and pattern width on the fabric. In the experiments, a fabric sample with a repetitive texture was tested by kinematic experiments, where the friction force maps were obtained by changing the preload and sliding velocity. Furthermore, the texture of the sample was revealed by using the spectral domain of the friction, where the fundamental frequency carries information regarding pattern dimension.

Section snippets

Psychophysical tests

Over the last decade, extensive research has been carried out on human skin friction [59]. The human finger is one of the most investigated parts of the human body due to its unique tactile feedback capabilities. In previous studies, the frictional behavior of the finger pad with different substrates was considered experimentally in vivo [31,39,60,61]. In these studies, the finger was kept constant in a fixed position, while the substrate moved at constant velocity. On the contrary, the

Comparison of on-off and feedback control in frequency domain

As a first step, sliding experiments were performed on repetitive textile fabric with an on-off and feedback controller, respectively. The results were investigated in the frequency domain, where a Fast Fourier Transform (FFT) routine with a Hamming window was applied to the friction data. The sampling frequency of the experiments varied between 500 Hz and 1000 Hz, depending on the velocity to obtain a higher frequency resolution and to reduce the leakage in the spectral analysis. Fig. 8 shows

Conclusion

In this study, touch psychological tests were conducted on adhesive elastomer and fabric surfaces to show the regulating behavior of human beings. The preload regulating behavior was more dominant on the adhesive elastomer than on the fabric, where the stick-slip phenomena govern the frictional dynamics. On the other hand, touch evaluation of fabrics resulted in a nearly constant normal load. Besides, the average friction and normal force values provided a reference range for the artificial

Acknowledgements

This work was supported by the Scientific Research Department of Hacettepe University [grant number FBB-2017-15679].

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